Baseline model: GBM
Advanced models: SVM, RandomForest, DNN(best), CNN, Xgboost
if(!require("EBImage")){
install.packages("BiocManager")
BiocManager::install("EBImage")
}
if(!require("R.matlab")){
install.packages("R.matlab")
}
if(!require("readxl")){
install.packages("readxl")
}
if(!require("dplyr")){
install.packages("dplyr")
}
if(!require("readxl")){
install.packages("readxl")
}
if(!require("ggplot2")){
install.packages("ggplot2")
}
if(!require("caret")){
install.packages("caret")
}
if(!require("glmnet")){
install.packages("glmnet")
}
if(!require("WeightedROC")){
install.packages("WeightedROC")
}
if(!require("gbm")){
install.packages("gbm")
}
if(!require("xgboost")){
install.packages("xgboost")
}
if(!require("caret")){
install.packages("caret")
}
# Install Miniconda (https://docs.conda.io/en/latest/miniconda.html)
if(!require("keras")){
install.packages("keras")
}
if(!require("tensorflow")){
install.packages("tensorflow")
install_tensorflow()
}
use_condaenv("r-tensorflow")
library(keras)
library(tensorflow)
library(R.matlab)
library(readxl)
library(dplyr)
library(EBImage)
library(ggplot2)
library(caret)
library(glmnet)
library(WeightedROC)
library(gbm)
require(xgboost)
library(caret)
set.seed(2030)
# setwd("~/Project3-FacialEmotionRecognition/doc")
# here replace it with your own path or manually set it in RStudio to where this rmd file is located.
# use relative path for reproducibility
Provide directories for training images. Training images and Training fiducial points will be in different subfolders.
train_dir <- "../data/train_set/" # This will be modified for different data sets.
train_image_dir <- paste(train_dir, "images/", sep="")
train_pt_dir <- paste(train_dir, "points/", sep="")
train_label_path <- paste(train_dir, "label.csv", sep="")
In this chunk, we have a set of controls for the evaluation experiments.
# run.cv <- TRUE # run cross-validation on the training set
# sample.reweight <- TRUE # run sample reweighting in model training
# K <- 5 # number of CV folds
# run.feature.train <- TRUE # process features for training set
# run.test <- TRUE # run evaluation on an independent test set
# run.feature.test <- TRUE # process features for test set
sample.reweight <- TRUE # run sample reweighting in model training
K <- 5 # number of CV folds
run.feature.train <- TRUE # process features for training set
run.feature.test <- TRUE # process features for test set
run.cv_gbm <- TRUE # run GBM cross-validation on the training set
run.test_gbm <- TRUE # run GBM evaluation on an independent test set
run.cv_dnn <- FALSE # run DNN cross-validation on the training set
run.test_dnn <- TRUE # run DNN evaluation on an independent test set
Using cross-validation or independent test set evaluation, we compare the performance of models with different specifications. In this part, we tune parameter n.trees and shrinkage for GBM.
# GBM parameters
n.trees <- c(500, 1000)
shrinkage <- c(0.01, 0.05)
# DNN parameters
# lrs <- c(0.0002, 0.0005, 0.001)
lrs <- c(0.0002)
#train-test split
info <- read.csv(train_label_path)
n <- nrow(info)
n_train <- round(n*(4/5), 0)
train_idx <- sample(info$Index, n_train, replace = F)
test_idx <- setdiff(info$Index, train_idx)
If you choose to extract features from images, such as using Gabor filter, R memory will exhaust all images are read together. The solution is to repeat reading a smaller batch(e.g 100) and process them.
n_files <- length(list.files(train_image_dir))
image_list <- list()
for(i in 1:100){
image_list[[i]] <- readImage(paste0(train_image_dir, sprintf("%04d", i), ".jpg"))
}
Fiducial points are stored in matlab format. In this step, we read them and store them in a list.
#function to read fiducial points
#input: index
#output: matrix of fiducial points corresponding to the index
readMat.matrix <- function(index){
return(round(readMat(paste0(train_pt_dir, sprintf("%04d", index), ".mat"))[[1]],0))
}
#load fiducial points
fiducial_pt_list <- lapply(1:n_files, readMat.matrix)
save(fiducial_pt_list, file="../output/fiducial_pt_list.RData")
The follow plots show how pairwise distance between fiducial points can work as feature for facial emotion recognition.
Figure1
feature.R should be the wrapper for all your feature engineering functions and options. The function feature( ) should have options that correspond to different scenarios for your project and produces an R object that contains features and responses that are required by all the models you are going to evaluate later.
feature.Rsource("../lib/feature.R")
tm_feature_train <- NA
if(run.feature.train){
tm_feature_train <- system.time(dat_train <- feature(fiducial_pt_list, train_idx))
save(dat_train, file="../output/feature_train.RData")
}else{
load(file="../output/feature_train.RData")
}
tm_feature_test <- NA
if(run.feature.test){
tm_feature_test <- system.time(dat_test <- feature(fiducial_pt_list, test_idx))
save(dat_test, file="../output/feature_test.RData")
}else{
load(file="../output/feature_test.RData")
}
Call the train model and test model from library.
train.R and test.R should be wrappers for all your model training steps and your classification/prediction steps.
train.R
test.R
source("../lib/train_gbm.R")
source("../lib/test_gbm.R")
source("../lib/cross_validation_gbm.R")
feature_train = as.matrix(dat_train[, -6007])
label_train = as.integer(dat_train$label)
if(run.cv_gbm){
res_cv_gbm <- matrix(0, nrow = length(n.trees) * length(shrinkage), ncol = 6)
count = 0
for(i in 1:length(n.trees)){
for(j in 1:length(shrinkage)){
count = count + 1
cat("n.trees =", n.trees[i], "\n")
cat("shrinkage =", shrinkage[j], "\n")
res_cv <- cv.function_gbm(features = feature_train, labels = label_train, K,
n.trees[i], shrinkage[j], reweight = sample.reweight)
res_cv_gbm[count,] <- c(n.trees[i], shrinkage[j], res_cv[1], res_cv[2], res_cv[3], res_cv[4])
}
}
colnames(res_cv_gbm) <- c("n.trees","shrinkage","mean_error", "sd_error", "mean_AUC", "sd_AUC")
save(res_cv_gbm, file="../output/res_cv_gbm.RData")
}else{
load("../output/res_cv_gbm.RData")
}
## n.trees = 500
## shrinkage = 0.01
## n.trees = 500
## shrinkage = 0.05
## n.trees = 1000
## shrinkage = 0.01
## n.trees = 1000
## shrinkage = 0.05
Visualize cross-validation results.
res_cv_gbm <- as.data.frame(res_cv_gbm)
if(run.cv_gbm){
p1 <- res_cv_gbm %>%
ggplot(aes(x = n.trees, y = mean_error,
ymin = mean_error - sd_error, ymax = mean_error + sd_error)) +
geom_crossbar() +
facet_wrap(~shrinkage)+
theme(axis.text.x = element_text(angle = 90, hjust = 1))
p2 <- res_cv_gbm %>%
ggplot(aes(x = n.trees, y = mean_AUC,
ymin = mean_AUC - sd_AUC, ymax = mean_AUC + sd_AUC)) +
geom_crossbar() +
facet_wrap(~shrinkage)+
theme(axis.text.x = element_text(angle = 90, hjust = 1))
print(p1)
print(p2)
}
# par_n.trees_best <- as.numeric(res_cv_gbm[which.min(res_cv_gbm$mean_error), 1])
# par_shrinkage_best <- as.numeric(res_cv_gbm[which.min(res_cv_gbm$mean_error), 2])
par_n.trees_best <- 500
par_shrinkage_best <- 0.05
feature_train = as.matrix(dat_train[, -6007])
label_train = as.integer(dat_train$label)
feature_test <- as.matrix(dat_test[, -6007])
# training weights
weight_train <- rep(NA, length(label_train))
for (v in unique(label_train)){
weight_train[label_train == v] = 0.5 * length(label_train) / length(label_train[label_train == v])
}
tm_train <- NA
if (sample.reweight){
tm_train <- system.time(fit_train_gbm <- train_gbm(feature_train, label_train, w = weight_train, par_n.trees_best, par_shrinkage_best))
} else {
tm_train <- system.time(fit_train_gbm <- train_gbm(feature_train, label_train, w = NULL, par_n.trees_best, par_shrinkage_best))
}
save(fit_train_gbm, file="../output/fit_train_gbm.RData")
tm_test = NA
feature_test <- as.matrix(dat_test[, -6007])
if(run.test_gbm){
load(file="../output/fit_train_gbm.RData")
tm_test <- system.time({prob_pred <- test_gbm(fit_train_gbm, feature_test, par_n.trees_best, pred.type = 'response');})
}
## reweight the test data to represent a balanced label distribution
label_test <- as.integer(dat_test$label)
weight_test <- rep(NA, length(label_test))
for (v in unique(label_test)){
weight_test[label_test == v] = 0.5 * length(label_test) / length(label_test[label_test == v])
}
label_pred <- ifelse(prob_pred > 0.5, 1, 0)
label_test <- ifelse(label_test == 2, 1, 0)
accu <- sum(weight_test * (label_pred == label_test)) / sum(weight_test)
tpr.fpr <- WeightedROC(prob_pred, label_test, weight_test)
auc <- WeightedAUC(tpr.fpr)
cat("The accuracy of model:", "GBM with n.trees =" , par_n.trees_best, "and shrinkage =", par_shrinkage_best, "is", accu*100, "%.\n")
## The accuracy of model: GBM with n.trees = 500 and shrinkage = 0.05 is 73.95539 %.
cat("The AUC of model:", "GBM with n.trees =" , par_n.trees_best, "and shrinkage =", par_shrinkage_best, "is", auc, ".\n")
## The AUC of model: GBM with n.trees = 500 and shrinkage = 0.05 is 0.8160487 .
Prediction performance matters, so does the running times for constructing features and for training the model, especially when the computation resource is limited.
cat("Time for constructing training features=", tm_feature_train[3], "s \n")
## Time for constructing training features= 0.68 s
cat("Time for constructing testing features=", tm_feature_test[3], "s \n")
## Time for constructing testing features= 0.12 s
cat("Time for training model=", tm_train[3], "s \n")
## Time for training model= 66.11 s
cat("Time for testing model=", tm_test[3], "s \n")
## Time for testing model= 0.08 s
Call the train model and test model from library.
train.R and test.R should be wrappers for all your model training steps and your classification/prediction steps.
train.R
test.R
source("../lib/train_dnn.R")
source("../lib/test_dnn.R")
DNN training takes long time. There are almost numberless parameters, layers, regulation functions, to play with. It is unrealistic to use a simple and straightforward cross-validation to illustrate. Instead we put our best DNN model here after tuning.
Visualize cross-validation results. N/A
# par_lr_best <- as.numeric(res_cv_dnn[which.min(res_cv_dnn$mean_error), 1])
par_lr_best = 0.0002
source("../lib/train_dnn.R")
source("../lib/test_dnn.R")
feature_train = as.matrix(dat_train[, -6007])
label_train = as.integer(dat_train$label)
feature_test <- as.matrix(dat_test[, -6007])
label_test <- as.integer(dat_test$label)
label_test <- ifelse(label_test == 2, 1, 0)
weight_test <- rep(NA, length(label_test))
# training weights
weight_train <- rep(NA, length(label_train))
for (v in unique(label_train)){
weight_train[label_train == v] = 0.5 * length(label_train) / length(label_train[label_train == v])
}
for (v in unique(label_test)){
weight_test[label_test == v] = 0.5 * length(label_test) / length(label_test[label_test == v])
}
tm_train <- NA
if (sample.reweight){
tm_train <- system.time(fit_train_dnn <- train_dnn(feature_train, label_train, w = weight_train, feature_test, label_test, weight_test, par_lr_best))
} else {
tm_train <- system.time(fit_train_dnn <- train_dnn(feature_train, label_train, w = NULL, feature_test, label_test, weight_test = NULL, par_lr_best))
}
save_model_hdf5(fit_train_dnn, "../output/fit_train_dnn.h5")
tm_test = NA
feature_test <- as.matrix(dat_test[, -6007])
if(run.test_dnn){
fit_train_dnn <- load_model_hdf5("../output/fit_train_dnn.h5")
tm_test <- system.time({label_pred <- test_dnn(fit_train_dnn, feature_test, type = "predict_classes");})
prob_pred <- test_dnn(fit_train_dnn, feature_test, type = "predict_proba")
}
## reweight the test data to represent a balanced label distribution
label_test <- as.integer(dat_test$label)
weight_test <- rep(NA, length(label_test))
for (v in unique(label_test)){
weight_test[label_test == v] = 0.5 * length(label_test) / length(label_test[label_test == v])
}
label_test <- ifelse(label_test == 2, 1, 0)
accu <- sum(weight_test * (label_pred == label_test)) / sum(weight_test)
# prob_pred <- apply(prob_pred, 1, max)
prob_pred <- prob_pred[, 2]
tpr.fpr <- WeightedROC(prob_pred, label_test, weight_test)
auc <- WeightedAUC(tpr.fpr)
cat("The accuracy of model:", "DNN with lr =", par_lr_best, "is", accu*100, "%.\n")
## The accuracy of model: DNN with lr = 2e-04 is 74.30359 %.
cat("The AUC of model:", "DNN", "is", auc, ".\n")
## The AUC of model: DNN is 0.8365958 .
Prediction performance matters, so does the running times for constructing features and for training the model, especially when the computation resource is limited.
cat("Time for constructing training features=", tm_feature_train[3], "s \n")
## Time for constructing training features= 0.68 s
cat("Time for constructing testing features=", tm_feature_test[3], "s \n")
## Time for constructing testing features= 0.12 s
cat("Time for training model=", tm_train[3], "s \n")
## Time for training model= 274.33 s
cat("Time for testing model=", tm_test[3], "s \n")
## Time for testing model= 0.44 s
###Reference - Du, S., Tao, Y., & Martinez, A. M. (2014). Compound facial expressions of emotion. Proceedings of the National Academy of Sciences, 111(15), E1454-E1462.